Proteomics Data Analysis Sample
Seun Awoniyi
December 19, 2022
pacman::p_load(rio, rafalib, pheatmap, dplyr, kableExtra, knitr, RColorBrewer,
ETLUtils, pheatmap, NormalyzerDE, MSnbase, data.table, sva,
ReactomeGSA, EnhancedVolcano, plotly, ggplot2, tidyr,
ggpubr, purrr, gplots, mice, VIM, UpSetR)1 Brief Information About Data
The data used in this analysis was part of my PhD project. The data is an output of MaxQuant, a tools use for quantitative analysis of large mass-spectrometric data. B cells that was transfected with Raft-Apex2 were activated with HEL, Fab2 at different time point. Followed by mass-spectrometry analysis.
Conditions compare in this analysis include:
- Fab2 5min activated Vs non-activated
- Fab2 10min activated Vs non-activated
- Fab2 15min activated Vs non-activated
- HEL 5min activated Vs non-activated
- HEL 10min activated Vs non-activated
- HEL 15min activated Vs non-activated
- Fab2 5min activated Vs HEL 5min activated
- Fab2 10min activated Vs HEL 10min activated
- Fab2 15min activated Vs HEL 15min activated
2 Exploratory Data Analysis
2.1 Data quality check
# Read raw file
raw = read.delim("data/proteinGroups.txt", stringsAsFactors = FALSE,
colClasses = "character")
head(raw) %>%
kbl(caption = "Table") %>%
kable_paper(bootstrap_options = "striped", full_width = F) %>%
kable_styling(fixed_thead = T, full_width = FALSE) %>%
scroll_box()| Protein.IDs | Majority.protein.IDs | Peptide.counts..all. | Peptide.counts..razor.unique. | Peptide.counts..unique. | Protein.names | Gene.names | Fasta.headers | Number.of.proteins | Peptides | Razor…unique.peptides | Unique.peptides | Peptides.10Fab2_1 | Peptides.10Fab2_2 | Peptides.10Fab2_3 | Peptides.10HEL_1 | Peptides.10HEL_2 | Peptides.10HEL_3 | Peptides.10NA_1 | Peptides.10NA_2 | Peptides.10NA_3 | Peptides.15Fab2_1 | Peptides.15Fab2_2 | Peptides.15Fab2_3 | Peptides.15HEL_1 | Peptides.15HEL_2 | Peptides.15HEL_3 | Peptides.15NA_1 | Peptides.15NA_2 | Peptides.15NA_3 | Peptides.5Fab2_1 | Peptides.5Fab2_2 | Peptides.5Fab2_3 | Peptides.5HEL_1 | Peptides.5HEL_2 | Peptides.5HEL_3 | Peptides.5NA_1 | Peptides.5NA_2 | Peptides.5NA_3 | Peptides.NB_1 | Peptides.NB_2 | Peptides.NB_3 | Razor…unique.peptides.10Fab2_1 | Razor…unique.peptides.10Fab2_2 | Razor…unique.peptides.10Fab2_3 | Razor…unique.peptides.10HEL_1 | Razor…unique.peptides.10HEL_2 | Razor…unique.peptides.10HEL_3 | Razor…unique.peptides.10NA_1 | Razor…unique.peptides.10NA_2 | Razor…unique.peptides.10NA_3 | Razor…unique.peptides.15Fab2_1 | Razor…unique.peptides.15Fab2_2 | Razor…unique.peptides.15Fab2_3 | Razor…unique.peptides.15HEL_1 | Razor…unique.peptides.15HEL_2 | Razor…unique.peptides.15HEL_3 | Razor…unique.peptides.15NA_1 | Razor…unique.peptides.15NA_2 | Razor…unique.peptides.15NA_3 | Razor…unique.peptides.5Fab2_1 | Razor…unique.peptides.5Fab2_2 | Razor…unique.peptides.5Fab2_3 | Razor…unique.peptides.5HEL_1 | Razor…unique.peptides.5HEL_2 | Razor…unique.peptides.5HEL_3 | Razor…unique.peptides.5NA_1 | Razor…unique.peptides.5NA_2 | Razor…unique.peptides.5NA_3 | Razor…unique.peptides.NB_1 | Razor…unique.peptides.NB_2 | Razor…unique.peptides.NB_3 | Unique.peptides.10Fab2_1 | Unique.peptides.10Fab2_2 | Unique.peptides.10Fab2_3 | Unique.peptides.10HEL_1 | Unique.peptides.10HEL_2 | Unique.peptides.10HEL_3 | Unique.peptides.10NA_1 | Unique.peptides.10NA_2 | Unique.peptides.10NA_3 | Unique.peptides.15Fab2_1 | Unique.peptides.15Fab2_2 | Unique.peptides.15Fab2_3 | Unique.peptides.15HEL_1 | Unique.peptides.15HEL_2 | Unique.peptides.15HEL_3 | Unique.peptides.15NA_1 | Unique.peptides.15NA_2 | Unique.peptides.15NA_3 | Unique.peptides.5Fab2_1 | Unique.peptides.5Fab2_2 | Unique.peptides.5Fab2_3 | Unique.peptides.5HEL_1 | Unique.peptides.5HEL_2 | Unique.peptides.5HEL_3 | Unique.peptides.5NA_1 | Unique.peptides.5NA_2 | Unique.peptides.5NA_3 | Unique.peptides.NB_1 | Unique.peptides.NB_2 | Unique.peptides.NB_3 | Sequence.coverage…. | Unique…razor.sequence.coverage…. | Unique.sequence.coverage…. | Mol..weight..kDa. | Sequence.length | Sequence.lengths | Fraction.average | Fraction.1 | Fraction.2 | Fraction.3 | Fraction.4 | Fraction.11 | Fraction.12 | Fraction.13 | Fraction.14 | Fraction.21 | Fraction.22 | Fraction.23 | Fraction.24 | Fraction.31 | Fraction.32 | Fraction.33 | Fraction.34 | Fraction.41 | Fraction.42 | Fraction.43 | Fraction.44 | Fraction.51 | Fraction.52 | Fraction.53 | Fraction.54 | Fraction.61 | Fraction.62 | Fraction.63 | Fraction.64 | Fraction.71 | Fraction.72 | Fraction.73 | Fraction.74 | Fraction.81 | Fraction.82 | Fraction.83 | Fraction.84 | Fraction.91 | Fraction.92 | Fraction.93 | Fraction.94 | Q.value | Score | Identification.type.10Fab2_1 | Identification.type.10Fab2_2 | Identification.type.10Fab2_3 | Identification.type.10HEL_1 | Identification.type.10HEL_2 | Identification.type.10HEL_3 | Identification.type.10NA_1 | Identification.type.10NA_2 | Identification.type.10NA_3 | Identification.type.15Fab2_1 | Identification.type.15Fab2_2 | Identification.type.15Fab2_3 | Identification.type.15HEL_1 | Identification.type.15HEL_2 | Identification.type.15HEL_3 | Identification.type.15NA_1 | Identification.type.15NA_2 | Identification.type.15NA_3 | Identification.type.5Fab2_1 | Identification.type.5Fab2_2 | Identification.type.5Fab2_3 | Identification.type.5HEL_1 | Identification.type.5HEL_2 | Identification.type.5HEL_3 | Identification.type.5NA_1 | Identification.type.5NA_2 | Identification.type.5NA_3 | Identification.type.NB_1 | Identification.type.NB_2 | Identification.type.NB_3 | Sequence.coverage.10Fab2_1…. | Sequence.coverage.10Fab2_2…. | Sequence.coverage.10Fab2_3…. | Sequence.coverage.10HEL_1…. | Sequence.coverage.10HEL_2…. | Sequence.coverage.10HEL_3…. | Sequence.coverage.10NA_1…. | Sequence.coverage.10NA_2…. | Sequence.coverage.10NA_3…. | Sequence.coverage.15Fab2_1…. | Sequence.coverage.15Fab2_2…. | Sequence.coverage.15Fab2_3…. | Sequence.coverage.15HEL_1…. | Sequence.coverage.15HEL_2…. | Sequence.coverage.15HEL_3…. | Sequence.coverage.15NA_1…. | Sequence.coverage.15NA_2…. | Sequence.coverage.15NA_3…. | Sequence.coverage.5Fab2_1…. | Sequence.coverage.5Fab2_2…. | Sequence.coverage.5Fab2_3…. | Sequence.coverage.5HEL_1…. | Sequence.coverage.5HEL_2…. | Sequence.coverage.5HEL_3…. | Sequence.coverage.5NA_1…. | Sequence.coverage.5NA_2…. | Sequence.coverage.5NA_3…. | Sequence.coverage.NB_1…. | Sequence.coverage.NB_2…. | Sequence.coverage.NB_3…. | Intensity | Intensity.10Fab2_1 | Intensity.10Fab2_2 | Intensity.10Fab2_3 | Intensity.10HEL_1 | Intensity.10HEL_2 | Intensity.10HEL_3 | Intensity.10NA_1 | Intensity.10NA_2 | Intensity.10NA_3 | Intensity.15Fab2_1 | Intensity.15Fab2_2 | Intensity.15Fab2_3 | Intensity.15HEL_1 | Intensity.15HEL_2 | Intensity.15HEL_3 | Intensity.15NA_1 | Intensity.15NA_2 | Intensity.15NA_3 | Intensity.5Fab2_1 | Intensity.5Fab2_2 | Intensity.5Fab2_3 | Intensity.5HEL_1 | Intensity.5HEL_2 | Intensity.5HEL_3 | Intensity.5NA_1 | Intensity.5NA_2 | Intensity.5NA_3 | Intensity.NB_1 | Intensity.NB_2 | Intensity.NB_3 | LFQ.intensity.10Fab2_1 | LFQ.intensity.10Fab2_2 | LFQ.intensity.10Fab2_3 | LFQ.intensity.10HEL_1 | LFQ.intensity.10HEL_2 | LFQ.intensity.10HEL_3 | LFQ.intensity.10NA_1 | LFQ.intensity.10NA_2 | LFQ.intensity.10NA_3 | LFQ.intensity.15Fab2_1 | LFQ.intensity.15Fab2_2 | LFQ.intensity.15Fab2_3 | LFQ.intensity.15HEL_1 | LFQ.intensity.15HEL_2 | LFQ.intensity.15HEL_3 | LFQ.intensity.15NA_1 | LFQ.intensity.15NA_2 | LFQ.intensity.15NA_3 | LFQ.intensity.5Fab2_1 | LFQ.intensity.5Fab2_2 | LFQ.intensity.5Fab2_3 | LFQ.intensity.5HEL_1 | LFQ.intensity.5HEL_2 | LFQ.intensity.5HEL_3 | LFQ.intensity.5NA_1 | LFQ.intensity.5NA_2 | LFQ.intensity.5NA_3 | LFQ.intensity.NB_1 | LFQ.intensity.NB_2 | LFQ.intensity.NB_3 | MS.MS.count.10Fab2_1 | MS.MS.count.10Fab2_2 | MS.MS.count.10Fab2_3 | MS.MS.count.10HEL_1 | MS.MS.count.10HEL_2 | MS.MS.count.10HEL_3 | MS.MS.count.10NA_1 | MS.MS.count.10NA_2 | MS.MS.count.10NA_3 | MS.MS.count.15Fab2_1 | MS.MS.count.15Fab2_2 | MS.MS.count.15Fab2_3 | MS.MS.count.15HEL_1 | MS.MS.count.15HEL_2 | MS.MS.count.15HEL_3 | MS.MS.count.15NA_1 | MS.MS.count.15NA_2 | MS.MS.count.15NA_3 | MS.MS.count.5Fab2_1 | MS.MS.count.5Fab2_2 | MS.MS.count.5Fab2_3 | MS.MS.count.5HEL_1 | MS.MS.count.5HEL_2 | MS.MS.count.5HEL_3 | MS.MS.count.5NA_1 | MS.MS.count.5NA_2 | MS.MS.count.5NA_3 | MS.MS.count.NB_1 | MS.MS.count.NB_2 | MS.MS.count.NB_3 | MS.MS.count | Peptide.sequences | Only.identified.by.site | Reverse | Potential.contaminant | id | Peptide.IDs | Peptide.is.razor | Mod..peptide.IDs | Evidence.IDs | MS.MS.IDs | Best.MS.MS | Biotinylation.by.BP.site.IDs | Oxidation..M..site.IDs | Biotinylation.by.BP.site.positions | Oxidation..M..site.positions | Taxonomy.IDs |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| A2A432 | A2A432 | 24 | 24 | 16 | Cullin-4B | Cul4b | sp|A2A432|CUL4B_MOUSE Cullin-4B OS=Mus musculus OX=10090 GN=Cul4b PE=1 SV=1 | 1 | 24 | 24 | 16 | 6 | 5 | 8 | 11 | 9 | 10 | 10 | 6 | 8 | 8 | 8 | 7 | 7 | 6 | 9 | 8 | 7 | 10 | 6 | 6 | 6 | 10 | 7 | 10 | 8 | 7 | 10 | 0 | 0 | 0 | 6 | 5 | 8 | 11 | 9 | 10 | 10 | 6 | 8 | 8 | 8 | 7 | 7 | 6 | 9 | 8 | 7 | 10 | 6 | 6 | 6 | 10 | 7 | 10 | 8 | 7 | 10 | 0 | 0 | 0 | 4 | 3 | 5 | 6 | 7 | 6 | 6 | 4 | 4 | 6 | 6 | 4 | 4 | 4 | 5 | 4 | 4 | 5 | 4 | 4 | 4 | 6 | 4 | 6 | 5 | 5 | 7 | 0 | 0 | 0 | 29 | 29 | 19.6 | 110.7 | 970 | 970 | 51.2 | 7 | 21 | 12 | 9 | 22 | 16 | 1 | 7 | 14 | 7 | 1 | 11 | 22 | 4 | 1 | 11 | 20 | 1 | 1 | 17 | 24 | 12 | 19 | 11 | 2 | 13 | 26 | 2 | 9 | 17 | 4 | 1 | 0 | 119.34 | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | 7.6 | 6.4 | 9.9 | 13 | 10.2 | 11.6 | 12.3 | 7.8 | 9.9 | 9.6 | 9.1 | 8.8 | 8.1 | 7.2 | 10.4 | 9.2 | 7.8 | 12.6 | 7.9 | 7.7 | 7.9 | 12.5 | 8.8 | 11.5 | 9.6 | 7.8 | 12 | 0 | 0 | 0 | 5250600000 | 169730000 | 105660000 | 301940000 | 270470000 | 140050000 | 176790000 | 425200000 | 139640000 | 253270000 | 393130000 | 145790000 | 190400000 | 114110000 | 132250000 | 175870000 | 303540000 | 67076000 | 221670000 | 177730000 | 57706000 | 71492000 | 295930000 | 60818000 | 211400000 | 214060000 | 56391000 | 378470000 | 0 | 0 | 0 | 34213000 | 17305000 | 57218000 | 38780000 | 27667000 | 38453000 | 34639000 | 9452900 | 36741000 | 56092000 | 34132000 | 34190000 | 36821000 | 32517000 | 43556000 | 24232000 | 31234000 | 41249000 | 43034000 | 27963000 | 26364000 | 46345000 | 20189000 | 34930000 | 46254000 | 49181000 | 32219000 | 0 | 0 | 0 | 4 | 0 | 5 | 6 | 2 | 2 | 3 | 1 | 4 | 5 | 1 | 4 | 3 | 2 | 6 | 2 | 1 | 7 | 5 | 1 | 1 | 5 | 1 | 3 | 7 | 0 | 5 | 0 | 0 | 0 | 86 | AFGSTIVINPEK;AGNKEATDEELEK;AHIISDQK;DKENPNQYNYIA;EATDEELEK;EDSLDSVLFLK;ETVEEQASTTER;EVPEYLHHVNK;GKDIEDGDKFICNDDFK;HATGIEDGELRR;LITYLDQTTQK;LKHECGAAFTSK;LPENYTDETWQK;LYAAEGQK;QLLGEHLTAILQK;QYQIDAAIVR;RPNKPAELIAK;SATDGNTSTTPPTSAK;SIFLFLDR;SILISSVASVHHANGLAK;SSTAVSSFANSKPGSAK;TIDGILLLIER;TLQSLACGK;YNLEELYQAVENLCSHK | 0 | 734;915;993;3585;4649;4791;6196;6334;8365;9402;14173;14233;15003;16199;18730;19317;19602;19849;20507;20532;21558;22609;22945;26942 | True;True;True;True;True;True;True;True;True;True;True;True;True;True;True;True;True;True;True;True;True;True;True;True | 761;947;1025;3713;4829;4976;6424;6570;8664;9725;14641;14703;15497;16723;19489;20094;20390;20643;21325;21350;22402;23488;23836;27966 | 18148;22531;22532;22533;22534;22535;22536;22537;22538;22539;22540;24723;24724;24725;24726;24727;24728;24729;24730;24731;24732;24733;24734;24735;24736;24737;24738;24739;24740;24741;24742;24743;24744;24745;24746;24747;24748;24749;83628;83629;106661;110177;110178;141090;141091;141092;141093;141094;141095;141096;141097;141098;141099;141100;141101;141102;141103;141104;141105;141106;141107;141108;141109;141110;141111;141112;141113;141114;141115;141116;141117;141118;141119;141120;141121;141122;141123;141124;141125;141126;141127;141128;141129;141130;141131;141132;141133;144187;144188;144189;144190;144191;144192;144193;144194;144195;144196;144197;144198;144199;144200;144201;144202;144203;144204;144205;144206;144207;196723;223498;223499;223500;223501;223502;223503;223504;223505;223506;223507;223508;223509;223510;223511;223512;223513;348775;348776;350579;350580;350581;368439;368440;368441;368442;368443;368444;368445;395692;395693;395694;395695;395696;395697;395698;395699;395700;395701;395702;395703;395704;395705;395706;395707;395708;395709;395710;395711;395712;395713;395714;395715;395716;395717;395718;395719;395720;395721;395722;395723;395724;395725;395726;395727;395728;395729;395730;458871;458872;458873;473873;473874;473875;473876;473877;473878;473879;473880;480812;480813;480814;480815;480816;480817;480818;480819;480820;480821;480822;480823;480824;480825;480826;480827;480828;480829;480830;480831;480832;480833;480834;480835;485931;503662;503663;503664;503665;503666;503667;503668;503669;503670;503671;503672;503673;503674;503675;503676;503677;503678;503679;503680;503681;503682;503683;503684;503685;503686;503687;503688;503689;503690;503691;503692;503693;504031;530381;530382;530383;530384;530385;530386;530387;530388;530389;530390;530391;530392;530393;530394;530395;530396;530397;530398;530399;530400;530401;530402;530403;530404;530405;530406;530407;530408;530409;530410;530411;530412;530413;530414;530415;530416;530417;530418;530419;530420;530421;530422;530423;530424;530425;530426;530427;530428;530429;530430;530431;530432;530433;530434;556562;565434;565435;565436;565437;565438;565439;565440;565441;565442;565443;565444;565445;565446;565447;664027;664028;664029;664030;664031;664032;664033;664034;664035;664036;664037;664038;664039;664040;664041;664042;664043;664044;664045;664046;664047;664048;664049;664050;664051;664052;664053;664054;664055;664056;664057 | 11855;14544;14545;14546;15807;15808;15809;15810;15811;15812;15813;55685;55686;70513;73000;92415;92416;92417;92418;92419;92420;92421;92422;92423;92424;92425;92426;92427;92428;92429;92430;92431;92432;94795;94796;94797;137324;156071;156072;156073;156074;244437;244438;245658;245659;257849;257850;257851;257852;275570;275571;275572;275573;275574;275575;275576;275577;275578;275579;275580;322449;332631;332632;332633;332634;336852;336853;336854;336855;336856;336857;336858;336859;336860;339841;353299;353300;353301;353302;353303;353304;353305;353306;353307;353308;353516;374853;374854;374855;374856;374857;374858;374859;374860;374861;374862;374863;374864;374865;374866;374867;374868;374869;374870;374871;374872;393892;399847;399848;399849;399850;399851;399852;399853;399854;466195;466196;466197;466198;466199;466200;466201;466202;466203;466204;466205;466206;466207;466208;466209;466210;466211;466212;466213;466214 | 11855;14545;15809;55685;70513;73000;92424;94796;137324;156073;244438;245659;257850;275573;322449;332631;336854;339841;353304;353516;374869;393892;399851;466214 | 10090 | ||||||||||||||||||
| A2A5R2 | A2A5R2 | 13 | 7 | 7 | Brefeldin A-inhibited guanine nucleotide-exchange protein 2 | Arfgef2 | sp|A2A5R2|BIG2_MOUSE Brefeldin A-inhibited guanine nucleotide-exchange protein 2 OS=Mus musculus OX=10090 GN=Arfgef2 PE=1 SV=1 | 1 | 13 | 7 | 7 | 3 | 3 | 3 | 2 | 1 | 1 | 1 | 2 | 3 | 3 | 1 | 3 | 1 | 2 | 2 | 2 | 1 | 1 | 1 | 1 | 1 | 2 | 2 | 3 | 3 | 1 | 4 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 2 | 0 | 2 | 0 | 0 | 0 | 1 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 2 | 0 | 2 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 2 | 0 | 2 | 0 | 0 | 0 | 10 | 6.2 | 6.2 | 202.24 | 1792 | 1792 | 47.6 | 4 | 3 | 2 | 1 | 4 | 1 | 1 | 3 | 1 | 0 | 13.298 | By matching | By MS/MS | By MS/MS | By MS/MS | By matching | By matching | By matching | By matching | By MS/MS | By MS/MS | By matching | By MS/MS | By matching | By matching | By matching | By MS/MS | By matching | By matching | By matching | By matching | By matching | By matching | By matching | By MS/MS | By MS/MS | By matching | By MS/MS | 2.7 | 2.7 | 2.7 | 1.9 | 1 | 1 | 1 | 1.1 | 2.5 | 2.9 | 1 | 2.9 | 1 | 1.5 | 1.5 | 1.6 | 1 | 1 | 1 | 1 | 1 | 1.8 | 1.8 | 2.9 | 3 | 1 | 3.6 | 0 | 0 | 0 | 246760000 | 8126100 | 9581900 | 12528000 | 7617400 | 0 | 0 | 0 | 0 | 3787200 | 50923000 | 0 | 11513000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 27476000 | 16592000 | 62866000 | 17064000 | 0 | 18683000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 5 | ALMEAVSHAK;ANFIEADKYFLPFELACQSK;CIAQMVSSQAANIR;CISQLELAQLIGTGVK;DAYVQALAR;GLECLVSILK;IVETICNCFQGPQTDEGVQLQIIK;LDGNAIVDFVR;QETSSLACCLR;QYLCVALSK;TCYNIYLASK;VGCNPNEDVAIFAVDSLR;WLCAVSMDELASPHHPR | 1 | 1443;1678;2840;2854;3193;8430;11804;13200;18377;19308;22152;24394;26242 | True;True;True;True;False;False;True;False;True;False;False;False;True | 1499;1752;2954;2968;3311;8729;12215;13649;19121;20085;23010;25349;27245 | 35395;35396;40472;40473;40474;40475;40476;40477;40478;67206;67207;67208;67209;67345;67346;67347;74776;74777;198311;198312;288174;288175;325329;325330;450634;473689;473690;544535;602576;602577;602578;602579;602580;602581;602582;602583;602584;602585;602586;602587;602588;602589;602590;602591;602592;602593;602594;602595;602596;602597;602598;602599;602600;602601;602602;602603;602604;602605;602606;602607;602608;602609;602610;602611;602612;602613;602614;602615;602616;602617;602618;647797 | 22844;26009;26010;26011;43747;43831;43832;49158;138405;204124;227606;227607;316461;332533;385254;425321;425322;425323;425324;425325;425326;425327;425328;425329;425330;425331;425332;425333;425334;425335;425336;425337;425338;425339;425340;425341;425342;425343;425344;453825 | 22844;26011;43747;43832;49158;138405;204124;227606;316461;332533;385254;425329;453825 | 10090 | |||||||||||||||||||||||||||||||||||||||||
| A2A791 | A2A791 | 2 | 2 | 2 | Zinc finger MYM-type protein 4 | Zmym4 | sp|A2A791|ZMYM4_MOUSE Zinc finger MYM-type protein 4 OS=Mus musculus OX=10090 GN=Zmym4 PE=1 SV=1 | 1 | 2 | 2 | 2 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 1.7 | 1.7 | 1.7 | 172.44 | 1549 | 1549 | 41.4 | 2 | 3 | 2 | 0.0030448 | 3.842 | By matching | By MS/MS | By MS/MS | By matching | By MS/MS | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.7 | 1 | 1.7 | 0 | 0 | 0 | 0 | 0 | 0 | 78354000 | 5114800 | 0 | 9560100 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 35416000 | 3815800 | 24447000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | AVAPQQGLLDR;SNAVQDADSELKPFSK | 2 | 2414;20998 | True;True | 2508;21833 | 57286;57287;517358;517359;517360;517361;517362 | 36804;366193;366194 | 36804;366193 | 10090 | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| A2AAE1 | A2AAE1 | 2 | 2 | 2 | Uncharacterized protein KIAA1109 | Kiaa1109 | sp|A2AAE1|K1109_MOUSE Transmembrane protein KIAA1109 OS=Mus musculus OX=10090 GN=Kiaa1109 PE=1 SV=4 | 1 | 2 | 2 | 2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.5 | 0.5 | 0.5 | 555.36 | 5005 | 5005 | 47.7 | 1 | 2 | 0.0022727 | 4.4298 | By MS/MS | By matching | By MS/MS | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.2 | 0 | 0.2 | 0 | 0 | 0.3 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 27456000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5984300 | 0 | 3333700 | 0 | 0 | 18138000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | GVVEETANNVDAGK;IGLQPAVLR | 3 | 9274;10678 | True;True | 9595;11034 | 220412;256916;256917 | 154015;178320 | 154015;178320 | 10090 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| A2AAJ9 | A2AAJ9 | 3 | 3 | 3 | Obscurin | Obscn | sp|A2AAJ9|OBSCN_MOUSE Obscurin OS=Mus musculus OX=10090 GN=Obscn PE=1 SV=3 | 1 | 3 | 3 | 3 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 2.1 | 2.1 | 2.1 | 966.58 | 8886 | 8886 | 49.4 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 1 | 0.0014138 | 4.9187 | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | 0 | 0 | 0 | 0.1 | 0 | 0.1 | 0 | 0.1 | 0 | 0.1 | 0 | 0 | 0.1 | 0.1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0.1 | 1.9 | 0 | 0.1 | 0 | 974810000 | 0 | 0 | 0 | 399150000 | 0 | 48428000 | 0 | 0 | 0 | 224130000 | 0 | 0 | 41789000 | 175830000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 19243000 | 28757000 | 0 | 37486000 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 1 | 0 | 2 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 8 | ADAGEYSCEAGGQK;ITPSEPQYSK;KEEATEGTMVTLR | 4 | 381;11734;12094 | True;True;True | 393;12143;12511 | 8197;286124;286125;286126;286127;286128;286129;296569;296570;296571 | 4946;202873;202874;202875;202876;209575;209576;209577 | 4946;202876;209577 | 10090 | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| A2AB59 | A2AB59 | 3 | 3 | 3 | Rho GTPase-activating protein 27 | Arhgap27 | sp|A2AB59|RHG27_MOUSE Rho GTPase-activating protein 27 OS=Mus musculus OX=10090 GN=Arhgap27 PE=1 SV=1 | 1 | 3 | 3 | 3 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 1 | 0 | 0 | 1 | 1 | 1 | 1 | 1 | 0 | 1 | 1 | 1 | 0 | 0 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5.6 | 5.6 | 5.6 | 97.047 | 869 | 869 | 57.6 | 1 | 3 | 1 | 2 | 1 | 2 | 3 | 1 | 1 | 1 | 2 | 0 | 8.4069 | By matching | By MS/MS | By MS/MS | By MS/MS | By matching | By matching | By matching | By MS/MS | By MS/MS | By MS/MS | By MS/MS | By MS/MS | 0 | 0 | 0 | 1.2 | 1.2 | 0 | 0.9 | 0 | 0 | 3.6 | 3.6 | 3.6 | 0.9 | 0.9 | 0 | 0.9 | 3.6 | 3.6 | 0 | 0 | 0 | 0 | 1.2 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 164660000 | 0 | 0 | 0 | 19601000 | 11259000 | 0 | 23271000 | 0 | 0 | 18397000 | 8924200 | 5405100 | 24449000 | 14005000 | 0 | 23050000 | 2859200 | 5382300 | 0 | 0 | 0 | 0 | 8055700 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 0 | 1 | 0 | 0 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 1 | 1 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 0 | 5 | AIAEGISELSADLLQGEEGEPSSADFGSSER;ISGNLATIQK;TSAAGGLR | 5 | 1032;11565;23303 | True;True;True | 1070;11970;24211 | 25669;25670;25671;25672;25673;25674;25675;281589;281590;281591;574296;574297;574298;574299;574300;574301;574302;574303 | 16284;16285;16286;199738;199739;405697;405698;405699;405700 | 16285;199739;405700 | 10090 |
Filter out Potential.contaminant, Reverse and Only.identified.by.site. Also select important features.
# Filter out contaminants hits
df = raw %>%
filter(Potential.contaminant != "+") %>%
filter(Reverse != "+") %>%
filter(Only.identified.by.site != "+")
#select intensity columns
df <- dplyr::select(df,Majority.protein.IDs,Gene.names, Protein.names,Unique.peptides,
"Unique.peptides.NB_1", "Unique.peptides.NB_2", "Unique.peptides.NB_3",
"Unique.peptides.5NA_1", "Unique.peptides.5NA_2", "Unique.peptides.5NA_3",
"Unique.peptides.10NA_1", "Unique.peptides.10NA_2", "Unique.peptides.10NA_3",
"Unique.peptides.15NA_1", "Unique.peptides.15NA_2", "Unique.peptides.15NA_3",
"Unique.peptides.5HEL_1", "Unique.peptides.5HEL_2", "Unique.peptides.5HEL_3",
"Unique.peptides.10HEL_1", "Unique.peptides.10HEL_2", "Unique.peptides.10HEL_3",
"Unique.peptides.15HEL_1", "Unique.peptides.15HEL_2", "Unique.peptides.15HEL_3",
"Unique.peptides.5Fab2_1", "Unique.peptides.5Fab2_2", "Unique.peptides.5Fab2_3",
"Unique.peptides.10Fab2_1", "Unique.peptides.10Fab2_2", "Unique.peptides.10Fab2_3",
"Unique.peptides.15Fab2_1", "Unique.peptides.15Fab2_2", "Unique.peptides.15Fab2_3",
"Intensity.NB_1", "Intensity.NB_2", "Intensity.NB_3",
"Intensity.5NA_1", "Intensity.5NA_2", "Intensity.5NA_3",
"Intensity.10NA_1", "Intensity.10NA_2", "Intensity.10NA_3",
"Intensity.15NA_1", "Intensity.15NA_2", "Intensity.15NA_3",
"Intensity.5HEL_1", "Intensity.5HEL_2", "Intensity.5HEL_3",
"Intensity.10HEL_1", "Intensity.10HEL_2", "Intensity.10HEL_3",
"Intensity.15HEL_1", "Intensity.15HEL_2", "Intensity.15HEL_3",
"Intensity.5Fab2_1", "Intensity.5Fab2_2", "Intensity.5Fab2_3",
"Intensity.10Fab2_1", "Intensity.10Fab2_2", "Intensity.10Fab2_3",
"Intensity.15Fab2_1", "Intensity.15Fab2_2", "Intensity.15Fab2_3",
Unique.peptides, Reverse, Potential.contaminant, Mol..weight..kDa.
)Only proteins that are identified with unique peptides > 2 are considered.
#filter out <2 unique peptide
df <- filter(df, Unique.peptides > 1) %>%
arrange(Gene.names)After MaxQuant run, 2636 proteins were identified.
print(nrow(raw) )## [1] 2636This was trimmed down to 2243 proteins after removing potential contaminants, proteins only identified by site, by reverse and proteins only identified with < 2 unique peptides.
#converts some columns to numeric
df[,4:67] <- sapply(df[,4:67], as.numeric)
#log transform intensity columns
df[,35:64] <- log2(df[,35:64])
#convert infinite to NA
df[mapply(is.infinite, df)] <- NA
df <- dplyr::select(df, Majority.protein.IDs, Gene.names, Protein.names, Unique.peptides,
"Intensity.NB_1", "Intensity.NB_2", "Intensity.NB_3",
"Intensity.5NA_1", "Intensity.5NA_2", "Intensity.5NA_3",
"Intensity.10NA_1", "Intensity.10NA_2", "Intensity.10NA_3",
"Intensity.15NA_1", "Intensity.15NA_2", "Intensity.15NA_3",
"Intensity.5HEL_1", "Intensity.5HEL_2", "Intensity.5HEL_3",
"Intensity.10HEL_1", "Intensity.10HEL_2", "Intensity.10HEL_3",
"Intensity.15HEL_1", "Intensity.15HEL_2", "Intensity.15HEL_3",
"Intensity.5Fab2_1", "Intensity.5Fab2_2", "Intensity.5Fab2_3",
"Intensity.10Fab2_1", "Intensity.10Fab2_2", "Intensity.10Fab2_3",
"Intensity.15Fab2_1", "Intensity.15Fab2_2", "Intensity.15Fab2_3",
Unique.peptides, Reverse, Potential.contaminant, Mol..weight..kDa.
)In “Majority.protein.IDs” column, some of the protein IDs for a specific protein are more than one. These multiple IDs are separated by semicolon. I need to chose only the first ID and discard the ones after semicolon.
#chhose first ID before semicolon
df$Majority.protein.IDs <- as.character(df$Majority.protein.IDs)
df$Majority.protein.IDs <- sub(';.*',"", df$Majority.protein.IDs)2.1.1 Intersections
Upset plot was then used to display intersection among different conditions of the identified proteins.
Non_Activated <- rep(1, length(df$Majority.protein.IDs))
Non_Activated[rowSums(is.na(df[8:16])) > 8] = 0
HEL_Act_5min <- rep(1, length(df$Majority.protein.IDs))
HEL_Act_5min[rowSums(is.na(df[17:19])) > 2] = 0
HEL_Act_10min <- rep(1, length(df$Majority.protein.IDs))
HEL_Act_10min[rowSums(is.na(df[20:22])) > 2] = 0
HEL_Act_15min <- rep(1, length(df$Majority.protein.IDs))
HEL_Act_15min[rowSums(is.na(df[23:25])) > 2] = 0
fab_Act_5min <- rep(1, length(df$Majority.protein.IDs))
fab_Act_5min[rowSums(is.na(df[26:28])) > 2] = 0
fab_Act_10min <- rep(1, length(df$Majority.protein.IDs))
fab_Act_10min[rowSums(is.na(df[29:31])) > 2] = 0
fab_Act_15min <- rep(1, length(df$Majority.protein.IDs))
fab_Act_15min[rowSums(is.na(df[32:34])) > 2] = 0
data_upset <- data.frame(df, Non_Activated, HEL_Act_5min, HEL_Act_10min, HEL_Act_15min,
fab_Act_5min, fab_Act_10min, fab_Act_15min) %>%
dplyr::select(Gene.names, Majority.protein.IDs, Non_Activated, HEL_Act_5min,
HEL_Act_10min, HEL_Act_15min, fab_Act_5min, fab_Act_10min, fab_Act_15min) %>%
rename("Non-Activated"="Non_Activated", "HEL-5min"="HEL_Act_5min", "HEL-10min"="HEL_Act_10min",
"HEL-15min"="HEL_Act_15min", "Fab2-5min"="fab_Act_5min", "Fab2-10min"="fab_Act_10min",
"Fab2-15min"="fab_Act_15min")
upSet <- upset(data_upset, sets = c("Non-Activated", "Fab2-5min", "Fab2-10min", "Fab2-15min",
"HEL-5min", "HEL-10min", "HEL-15min"),
keep.order= TRUE ,number.angles = 30, point.size = 2,
line.size = 0.5, mb.ratio = c(0.60, 0.40),order.by = "freq",
mainbar.y.label = "Protein hits intersection", sets.x.label = "Protein hits per conditions",
text.scale = c(1.3, 1.3, 1, 1, 2, 0.9))
upSet
Ensure that a protein has one intensity value per condition. Then ensure that the amount of NA per row is not greater that 14.
conf_df=subset(df,rowSums(is.na(df[8:16])) < 8 | rowSums(is.na(df[17:19])) < 2 |
rowSums(is.na(df[20:22])) < 2 | rowSums(is.na(df[23:25])) < 2 |
rowSums(is.na(df[26:28])) < 2 | rowSums(is.na(df[29:31])) < 2 |
rowSums(is.na(df[32:34])) < 2 )#at least 2 in one condition
conf_df=subset(conf_df, rowSums(is.na(conf_df[8:34])) < 14)#1 out of 3
row.names(conf_df) = conf_df$Majority.protein.IDs
gene_name <- conf_df$Gene.names
#remove some columns
conf_df <- conf_df %>% dplyr::select(-c("Majority.protein.IDs", "Gene.names", "Protein.names", "Unique.peptides",
"Intensity.NB_1", "Intensity.NB_2", "Intensity.NB_3",
"Reverse", "Potential.contaminant", "Mol..weight..kDa."))
#rename columns
colnames(conf_df) <- c("5NA_1", "5NA_2", "5NA_3", "10NA_1",
"10NA_2", "10NA_3", "15NA_1", "15NA_2",
"15NA_3", "5HEL_1", "5HEL_2", "5HEL_3",
"10HEL_1", "10HEL_2", "10HEL_3",
"15HEL_1", "15HEL_2", "15HEL_3",
"5Fab2_1", "5Fab2_2", "5Fab2_3",
"10Fab2_1", "10Fab2_2", "10Fab2_3",
"15Fab2_1", "15Fab2_2", "15Fab2_3")
my_label <- c("NA_", "NA_", "NA_", "NA_",
"NA_", "NA_", "NA_", "NA_",
"NA_", "5HEL", "5HEL", "5HEL",
"10HEL", "10HEL", "10HEL",
"15HEL", "15HEL", "15HEL",
"5Fab2", "5Fab2", "5Fab2",
"10Fab2", "10Fab2", "10Fab2",
"15Fab2", "15Fab2", "15Fab2")
my_label2 <- c("5NA", "5NA", "5NA", "10NA",
"10NA", "10NA", "15NA", "15NA",
"15NA", "5HEL", "5HEL", "5HEL",
"10HEL", "10HEL", "10HEL",
"15HEL", "15HEL", "15HEL",
"5Fab2", "5Fab2", "5Fab2",
"10Fab2", "10Fab2", "10Fab2",
"15Fab2", "15Fab2", "15Fab2")
# my_label2 <- c("5NA_1", "5NA_2", "5NA_3", "10NA_1",
# "10NA_2", "10NA_3", "15NA_1", "15NA_2",
# "15NA_3", "5HEL_1", "5HEL_2", "5HEL_3",
# "10HEL_1", "10HEL_2", "10HEL_3",
# "15HEL_1", "15HEL_2", "15HEL_3",
# "5Fab2_1", "5Fab2_2", "5Fab2_3",
# "10Fab2_1", "10Fab2_2", "10Fab2_3",
# "15Fab2_1", "15Fab2_2", "15Fab2_3")2.1.2 Check missing values
In total there are 27 conditions. To be less stringent, I had allowed max. 13 missing values per row. The plots below were used to evaluate the amount of missing values present in this data.
#From VIM package aggr function helps rank the variables in decreasing order of missing values.
# e.g variable ca has the highest amount of missing values.
mice_plot <- aggr(conf_df, col = c("green","blue"),
numbers=TRUE, sortVars = TRUE,
labels = names(conf_df), cex.axis = .7,
gap = 3, ylab = c("Missing data","Pattern")) # (VIM) display graphically missing values
##
## Variables sorted by number of missings:
## Variable Count
## 10Fab2_2 0.17955615
## 5NA_2 0.17148621
## 10Fab2_1 0.15063887
## 15NA_2 0.14794889
## 10NA_2 0.14660390
## 5Fab2_2 0.13449899
## 10HEL_2 0.13382650
## 10Fab2_3 0.13248151
## 15HEL_1 0.12642905
## 5Fab2_1 0.12508406
## 5Fab2_3 0.12172159
## 10NA_1 0.11970410
## 5HEL_2 0.11634163
## 10NA_3 0.11096167
## 5NA_1 0.10692670
## 10HEL_1 0.10558171
## 10HEL_3 0.10356422
## 15NA_1 0.09885676
## 5HEL_1 0.09347680
## 15NA_3 0.09213181
## 5HEL_3 0.08944183
## 5NA_3 0.07868191
## 15HEL_2 0.07464694
## 15HEL_3 0.06792199
## 15Fab2_2 0.06523201
## 15Fab2_1 0.05716207
## 15Fab2_3 0.04841964In the heat map on right side of the above plot, blue represents missing value while green represents non-missing value.
Note: I did not notice any striking pattern in the above evaluation of missing values across conditions. As a result i did not take any action regarding this.
2.2 Missing imputation and Normalization
Data was normalized using CycLoess (from NormalyzerDE) and missing values were imputed using kNN (nearest neighbor = 3). Both MDS plot and Hierarchical cluster plot were used to evaluate similarities among samples before and after importation.
2.2.1 MDS and Hierarchical cluster before normalization and imputation
myplclust(hclust(dist(t(conf_df))), labels = my_label2,
lab.col = as.fumeric(as.character(my_label2)))
MSD plot
group <- factor(as.character(my_label2))
d <- dist(t(conf_df))
mds <- cmdscale(d)
mypar()
plot(mds[,1], mds[,2], bg = as.numeric(group),pch = 21,
xlab="First dimension",ylab="Second dimension")
#legend("bottomleft",levels(group), col=seq(along=levels(group)),pch=15, text.width = 9, ncol = 3)
text(mds[,1], mds[,2], labels = group, cex= 0.6, pos=2)
group <- factor(as.character(my_label2))
d <- dist(t(conf_df))
mds <- cmdscale(d, k = 4)
mypar()
plot(mds[,3], mds[,4],bg = as.numeric(group),pch=21,
xlab="Third dimension",ylab="Forth dimension")
#legend("bottomleft",levels(group), col=seq(along=levels(group)),pch=15, text.width = 9, ncol = 3)
text(mds[,3], mds[,4], labels = group, cex= 0.6, pos=2)
From the above MSD plot, it seems the data clustered mostly based on days of experiments.
Note K was chosen using Elbow method.
#####Imputation kNN
conf_df$Uniprot = row.names(conf_df)
dat_impt=readMSnSet2(conf_df, ecol= 1:27, fnames = "Uniprot")#msnset mode
knn_imputation <- impute(dat_impt, "knn")#imputation
knn_imputation <- data.frame(knn_imputation)%>%t()%>%data.frame()#transpose
knn_imputation$Uniprot <- rownames(knn_imputation)#make rowname columns
#rio::export(knn_imputation, "data_imp.txt")2.2.2 Hierarchical cluster after imputation
group <- factor(as.character(my_label2))
d <- dist(t(data_impt[, 1:27]))
mds <- cmdscale(d)
mypar()
plot(mds[,1],mds[,2],bg=as.numeric(group),pch=21,
xlab="First dimension",ylab="Second dimension")
#legend("bottomleft",levels(group), col=seq(along=levels(group)),pch=15, text.width = 9, ncol = 3)
text(mds[,1],mds[,2], labels=group, cex= 0.6, pos=2)
After imputation, samples still seems to cluster based on days of experiments.
NormalyzerDE R package was used to select the best normalization method for this data. The best normalization method is CycLoess
#Normalization
dataFp <- "~/Desktop/APEX2_project2/data_imp.txt"
designFp <- "~/Desktop/APEX2_project2/design.txt"
outDir <- "~/Desktop/APEX2_project2"
normalyzer(jobName="quick_analysis", noLogTransform= TRUE, designPath=designFp,
dataPath=dataFp, outputDir=outDir)## You are running version 1.10.0 of NormalyzerDE## [Step 1/5] Load data and verify input## Input data checked. All fields are valid.## Sample check: More than one sample group found## Sample replication check: All samples have replicates## No RT column found, skipping RT processing## [Step 1/5] Input verified, job directory prepared at:~/Desktop/APEX2_project2/quick_analysis## [Step 2/5] Performing normalizations## VSN normalization assumes non log-transformed data, as the option noLogTransform is specified it is assumed to not need log2-transformation and thus the VSN normalization is skipped## No RT column specified (column named 'RT') or option not specified Skipping RT normalization.## [Step 2/5] Done!## [Step 3/5] Generating evaluation measures...## [Step 3/5] Done!## [Step 4/5] Writing matrices to file## [Step 4/5] Matrices successfully written## [Step 5/5] Generating plots...## [Step 5/5] Plots successfully generated## All done! Results are stored in: ~/Desktop/APEX2_project2/quick_analysis, processing time was 0.9 minutes2.2.3 MDS and Hierarchical cluster after imputation and Normalization
norm_df <- import("~/Desktop/APEX2_project2/quick_analysis/CycLoess-normalized.txt")
myplclust(hclust(dist(t(norm_df[, 2:28]))), labels = my_label2,
lab.col = as.fumeric(as.character(my_label2)))
MDS plot
d <- dist(t(norm_df[, 2:28]))
mds <- cmdscale(d)
mypar()
plot(mds[,1],mds[,2],bg=as.numeric(group), pch=21,
xlab="First dimension",ylab="Second dimension")
#legend("topleft",levels(group),col=seq(along=levels(group)),pch=15, text.width = 4, ncol = 3)
text(mds[,1],mds[,2], labels=group, cex= 0.6, pos=2)
d <- dist(t(norm_df[, 2:28]))
mds <- cmdscale(d, k = 4)
mypar()
plot(mds[,3],mds[,4],bg=as.numeric(group), pch=21,
xlab="Third dimension",ylab="Forth dimension")
#legend("topleft",levels(group),col=seq(along=levels(group)),pch=15, text.width = 4, ncol = 3)
text(mds[,3],mds[,4], labels=group, cex= 0.6, pos=2)
After normalization, samples start to cluster based on replicates
2.3 Batch correction
To remove batch effect I tried 3 different methods, Linear regression and SVA.
2.3.1 Linear regression
design = read.delim("~/Desktop/APEX2_project2/design_batch.txt")
row.names(design) = design$sample
mod = model.matrix(~as.factor(group)+ as.factor(Batch),data=design)
fit = lm.fit(mod,t(norm_df[2:28]))
fitted_values <- fit[["fitted.values"]]
data_batchCor_linear <- t(fitted_values)
data_batchCor_linear <- data.frame(data_batchCor_linear)
data_batchCor_linear$Uniprot <- norm_df$Uniprotmyplclust(hclust(dist(t(data_batchCor_linear[,1:27]))), labels = my_label2,
lab.col = as.fumeric(as.character(my_label2)))
MDS plot
group <- factor(as.character(my_label))
d <- dist(t(data_batchCor_linear[,1:27]))
mds <- cmdscale(d, k = 4)
mypar()
plot(mds[,1],mds[,2],bg=as.numeric(group),pch=21,
xlab="First dimension",ylab="Second dimension")
text(mds[,1],mds[,2], labels=group, cex= 0.6, pos=2)
#legend("topright",levels(group),col=seq(along=levels(group)), pch=15, text.width = 4, ncol = 3)
#legend("topleft",levels(group),col=seq(along=levels(group)),pch=15, text.width = 4, ncol = 3)group <- factor(as.character(my_label))
d <- dist(t(data_batchCor_linear[,1:27]))
mds <- cmdscale(d, k = 4)
mypar()
plot(mds[,3],mds[,4],bg= as.numeric(group),pch=21,
xlab="Third dimension",ylab="forth dimension")
text(mds[,3],mds[,4], labels=group, cex= 0.6, pos = 2)
#legend("bottomright",levels(group),col=seq(along=levels(group)), pch=15, text.width = 4, ncol = 3)
#legend("topleft",levels(group),col=seq(along=levels(group)),pch=15, text.width = 4, ncol = 3)2.3.2 SVA
row.names(norm_df) = norm_df$Uniprot
norm_df2 <- norm_df[, 2:28]
mod = model.matrix(~group,data = design)
mod0 = model.matrix(~1, data = design)
sva1 = sva(data.matrix(norm_df2), mod, mod0, n.sv = 2)## Number of significant surrogate variables is: 2
## Iteration (out of 5 ):1 2 3 4 5summary(lm(sva1$sv ~ design$Batch))## Response Y1 :
##
## Call:
## lm(formula = Y1 ~ design$Batch)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.10635 -0.06122 -0.02987 0.02314 0.34310
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 0.40359 0.05262 7.670 5.02e-08 ***
## design$Batch -0.20180 0.02436 -8.284 1.24e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1033 on 25 degrees of freedom
## Multiple R-squared: 0.733, Adjusted R-squared: 0.7223
## F-statistic: 68.63 on 1 and 25 DF, p-value: 1.236e-08
##
##
## Response Y2 :
##
## Call:
## lm(formula = Y2 ~ design$Batch)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.22908 -0.14193 -0.06845 0.15396 0.48328
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -0.18377 0.09378 -1.960 0.0613 .
## design$Batch 0.09189 0.04341 2.117 0.0444 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.1842 on 25 degrees of freedom
## Multiple R-squared: 0.152, Adjusted R-squared: 0.1181
## F-statistic: 4.48 on 1 and 25 DF, p-value: 0.04442modsv = cbind(mod, sva1$sv)
fitsv = lm.fit(modsv, t(data.matrix(norm_df2)))
fitted_values <- fitsv[["fitted.values"]]
data_batchCor_sva <- t(fitted_values)myplclust(hclust(dist(t(data_batchCor_sva))), labels = my_label2, lab.col = as.fumeric(as.character(my_label)))
MDS plot
group <- factor(as.character(my_label2))
d <- dist(t(data_batchCor_sva))
mds <- cmdscale(d, k = 4)
mypar()
plot(mds[,1],mds[,2],bg = as.numeric(group), pch = 21,
xlab="First dimension",ylab="Second dimension")
text(mds[,1],mds[,2], labels=group, cex= 0.6, pos = 2)
#legend("topright",levels(group),col=seq(along=levels(group)), pch=15, text.width = 4, ncol = 3)group <- factor(as.character(my_label2))
d <- dist(t(data_batchCor_sva))
mds <- cmdscale(d, k = 4)
mypar()
plot(mds[,3],mds[,4],bg=as.numeric(group),pch=21,
xlab="Third dimension",ylab="Forth dimension")
text(mds[,3],mds[,4], labels=group, cex= 0.6, pos=2)
#legend("topleft",levels(group),col=seq(along=levels(group)), pch=15, text.width = 4, ncol = 3)data_batchCor_sva <- data.frame(data_batchCor_sva)
data_batchCor_sva$Uniprot = row.names(data_batchCor_sva)
#rio::export(data_batchCor_sva, "data_batchCor_sva.txt")I then performed differential expression analysis using Limma after batch correct.
sessionInfo()## R version 4.1.0 (2021-05-18)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur 10.16
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_GB.UTF-8/en_GB.UTF-8/en_GB.UTF-8/C/en_GB.UTF-8/en_GB.UTF-8
##
## attached base packages:
## [1] grid stats4 parallel stats graphics grDevices utils
## [8] datasets methods base
##
## other attached packages:
## [1] UpSetR_1.4.0 VIM_6.1.1 colorspace_2.0-3
## [4] mice_3.14.0 gplots_3.1.1 purrr_0.3.4
## [7] ggpubr_0.4.0 tidyr_1.2.0 plotly_4.10.0
## [10] EnhancedVolcano_1.10.0 ggrepel_0.9.1 ggplot2_3.3.6
## [13] ReactomeGSA_1.6.1 sva_3.40.0 BiocParallel_1.26.2
## [16] genefilter_1.74.1 mgcv_1.8-38 nlme_3.1-153
## [19] data.table_1.14.2 MSnbase_2.18.0 ProtGenerics_1.24.0
## [22] S4Vectors_0.30.2 mzR_2.26.1 Rcpp_1.0.7
## [25] Biobase_2.52.0 BiocGenerics_0.38.0 NormalyzerDE_1.10.0
## [28] ETLUtils_1.5 ff_4.0.5 bit_4.0.4
## [31] RColorBrewer_1.1-3 knitr_1.36 kableExtra_1.3.4
## [34] dplyr_1.0.9 pheatmap_1.0.12 rafalib_1.0.0
## [37] rio_0.5.29
##
## loaded via a namespace (and not attached):
## [1] pacman_0.5.1 utf8_1.2.2
## [3] tidyselect_1.1.2 RSQLite_2.2.9
## [5] AnnotationDbi_1.54.1 htmlwidgets_1.5.4
## [7] ranger_0.13.1 munsell_0.5.0
## [9] codetools_0.2-18 preprocessCore_1.54.0
## [11] withr_2.5.0 highr_0.9
## [13] ggalt_0.4.0 rstudioapi_0.13
## [15] robustbase_0.93-9 vcd_1.4-9
## [17] ggsignif_0.6.3 Rttf2pt1_1.3.9
## [19] mzID_1.30.0 labeling_0.4.2
## [21] MatrixGenerics_1.4.3 GenomeInfoDbData_1.2.6
## [23] farver_2.1.1 bit64_4.0.5
## [25] vctrs_0.4.1 generics_0.1.3
## [27] xfun_0.28 R6_2.5.1
## [29] doParallel_1.0.16 GenomeInfoDb_1.28.4
## [31] ggbeeswarm_0.6.0 clue_0.3-60
## [33] locfit_1.5-9.4 MsCoreUtils_1.4.0
## [35] bitops_1.0-7 cachem_1.0.6
## [37] DelayedArray_0.18.0 assertthat_0.2.1
## [39] scales_1.2.0 nnet_7.3-16
## [41] beeswarm_0.4.0 gtable_0.3.0
## [43] ash_1.0-15 affy_1.70.0
## [45] sandwich_3.0-1 rlang_1.0.3
## [47] systemfonts_1.0.2 splines_4.1.0
## [49] rstatix_0.7.0 lazyeval_0.2.2
## [51] extrafontdb_1.0 impute_1.66.0
## [53] hexbin_1.28.2 broom_1.0.0
## [55] checkmate_2.0.0 BiocManager_1.30.18
## [57] yaml_2.2.1 abind_1.4-5
## [59] backports_1.4.1 Hmisc_4.6-0
## [61] extrafont_0.17 tools_4.1.0
## [63] affyio_1.62.0 ellipsis_0.3.2
## [65] raster_3.5-2 jquerylib_0.1.4
## [67] proxy_0.4-26 plyr_1.8.6
## [69] base64enc_0.1-3 zlibbioc_1.38.0
## [71] RCurl_1.98-1.5 rpart_4.1-15
## [73] zoo_1.8-9 SummarizedExperiment_1.22.0
## [75] haven_2.4.3 cluster_2.1.2
## [77] magrittr_2.0.3 openxlsx_4.2.5
## [79] lmtest_0.9-39 pcaMethods_1.84.0
## [81] matrixStats_0.61.0 hms_1.1.1
## [83] RcmdrMisc_2.7-1 evaluate_0.14
## [85] xtable_1.8-4 XML_3.99-0.10
## [87] jpeg_0.1-9 readxl_1.3.1
## [89] IRanges_2.26.0 gridExtra_2.3
## [91] compiler_4.1.0 tibble_3.1.7
## [93] maps_3.4.0 KernSmooth_2.23-20
## [95] ncdf4_1.19 crayon_1.5.1
## [97] htmltools_0.5.2 Formula_1.2-4
## [99] DBI_1.1.1 proj4_1.0-10.1
## [101] MASS_7.3-54 boot_1.3-28
## [103] Matrix_1.3-4 car_3.0-12
## [105] cli_3.3.0 vsn_3.60.0
## [107] GenomicRanges_1.44.0 forcats_0.5.1
## [109] pkgconfig_2.0.3 laeken_0.5.2
## [111] foreign_0.8-81 sp_1.4-6
## [113] terra_1.4-22 MALDIquant_1.21
## [115] xml2_1.3.3 foreach_1.5.1
## [117] svglite_2.0.0 annotate_1.70.0
## [119] vipor_0.4.5 bslib_0.3.1
## [121] webshot_0.5.2 XVector_0.32.0
## [123] rvest_1.0.2 stringr_1.4.0
## [125] digest_0.6.29 Biostrings_2.60.2
## [127] rmarkdown_2.11 cellranger_1.1.0
## [129] htmlTable_2.3.0 nortest_1.0-4
## [131] edgeR_3.34.1 curl_4.3.2
## [133] gtools_3.9.2 lifecycle_1.0.1
## [135] jsonlite_1.7.2 carData_3.0-4
## [137] viridisLite_0.4.0 limma_3.48.3
## [139] fansi_1.0.3 pillar_1.7.0
## [141] lattice_0.20-45 DEoptimR_1.0-9
## [143] ggrastr_1.0.1 KEGGREST_1.32.0
## [145] fastmap_1.1.0 httr_1.4.2
## [147] survival_3.2-13 glue_1.6.2
## [149] zip_2.2.0 png_0.1-7
## [151] iterators_1.0.13 class_7.3-19
## [153] stringi_1.7.8 sass_0.4.0
## [155] blob_1.2.2 latticeExtra_0.6-29
## [157] caTools_1.18.2 memoise_2.0.1
## [159] ape_5.5 e1071_1.7-9